Carney Ffion, Long Alfie, Kandt Jens
The Bartlett Centre for Advanced Spatial Analysis, University College London, London, United Kingdom.
Front Big Data. 2022 May 23;5:867085. doi: 10.3389/fdata.2022.867085. eCollection 2022.
Using smart card travel data, we compare demand for bus services by passengers of age 65 or older prior to and during the COVID-19 pandemic to identify public transport-reliant users residing in more car-dependent environments-i.e., people who rely on public transport services to carry out essential activities, such as daily shopping and live in areas with low public transport accessibility. Viewing lockdowns as natural experiments, we use spatial analysis combined with multilevel logistic regressions to characterize the demographic and geographic context of those passengers who continued to use public transport services in these areas during lockdown periods, or quickly returned to public transport when restrictions were eased. We find that this particular type of public transport reliance is significantly associated with socio-demographic characteristics alongside urban residential conditions. Specifically, we identify suburban geographies of public transport reliance, which are at risk of being overlooked in approaches that view public transport dependence mainly as an outcome of deprivation. Our research demonstrates once again that inclusive, healthy and sustainable mobility can only be achieved if all areas of metropolitan regions are well and reliably served by public transport.
利用智能卡出行数据，我们比较了65岁及以上乘客在新冠疫情之前和期间对公交服务的需求，以识别居住在汽车依赖程度较高环境中的公共交通依赖型用户，即那些依靠公共交通服务开展日常购物等基本活动且居住在公共交通可达性较低地区的人群。将封锁视为自然实验，我们运用空间分析结合多层次逻辑回归来描述在封锁期间继续使用公共交通服务或在限制放宽后迅速恢复使用公共交通的乘客的人口统计学和地理背景。我们发现，这种特殊类型的公共交通依赖与社会人口特征以及城市居住条件显著相关。具体而言，我们识别出了公共交通依赖的郊区地域，而在主要将公共交通依赖视为贫困结果的研究方法中，这些地域可能会被忽视。我们的研究再次表明，只有当大都市地区的所有区域都能得到良好且可靠的公共交通服务时，才能实现包容、健康和可持续的出行。
